Spatial-temporal sensor data imputation in traffic data modelling
The traffic data corrupted by missing data significantly limit the robustness of traffic modelling. Our research aims to develop modelling techniques for data imputation tasks. Our first work is to impute speed data by an RBF based fitting approach for multivariate data matrixes with irregular locat...
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2023
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sg-ntu-dr.10356-1678602023-06-01T08:00:48Z Spatial-temporal sensor data imputation in traffic data modelling Nie, Helei Zheng Jianmin School of Computer Science and Engineering ASJMZheng@ntu.edu.sg Engineering::Computer science and engineering The traffic data corrupted by missing data significantly limit the robustness of traffic modelling. Our research aims to develop modelling techniques for data imputation tasks. Our first work is to impute speed data by an RBF based fitting approach for multivariate data matrixes with irregular location graphs. The results show that RBF approach is capable of imputing missing values, especially in a short missing span. By incorporating the available dimensions of the data, RBF method can utilize the information from time and space dimensions for effective imputation with higher missing ratios. Our second work is to propose a deep learning imputation model with a Stacking STGCN Auto-Encoder structure. We develop an adaptive graph convolution technique for to effectively utilize data missing status when traffic data suffer from severe missing conditions. The experiments show promising results when the traffic data contain large random missing regions in the time domain. Master of Engineering 2023-05-16T02:56:35Z 2023-05-16T02:56:35Z 2023 Thesis-Master by Research Nie, H. (2023). Spatial-temporal sensor data imputation in traffic data modelling. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167860 https://hdl.handle.net/10356/167860 10.32657/10356/167860 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Nie, Helei Spatial-temporal sensor data imputation in traffic data modelling |
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The traffic data corrupted by missing data significantly limit the robustness of traffic modelling. Our research aims to develop modelling techniques for data imputation tasks. Our first work is to impute speed data by an RBF based fitting approach for multivariate data matrixes with irregular location graphs. The results show that RBF approach is capable of imputing missing values, especially in a short missing span. By incorporating the available dimensions of the data, RBF method can utilize the information from time and space dimensions for effective imputation with higher missing ratios. Our second work is to propose a deep learning imputation model with a Stacking STGCN Auto-Encoder structure. We develop an adaptive graph convolution technique for to effectively utilize data missing status when traffic data suffer from severe missing conditions. The experiments show promising results when the traffic data contain large random missing regions in the time domain. |
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Zheng Jianmin |
author_facet |
Zheng Jianmin Nie, Helei |
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Thesis-Master by Research |
author |
Nie, Helei |
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Nie, Helei |
title |
Spatial-temporal sensor data imputation in traffic data modelling |
title_short |
Spatial-temporal sensor data imputation in traffic data modelling |
title_full |
Spatial-temporal sensor data imputation in traffic data modelling |
title_fullStr |
Spatial-temporal sensor data imputation in traffic data modelling |
title_full_unstemmed |
Spatial-temporal sensor data imputation in traffic data modelling |
title_sort |
spatial-temporal sensor data imputation in traffic data modelling |
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Nanyang Technological University |
publishDate |
2023 |
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https://hdl.handle.net/10356/167860 |
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1772828890524286976 |